The authors of EfficientNet [1]} illustrated that model scaling can be achieved by carefully balancing network depth, width, and resolution, leading to a better performance with the fixed amount of computation resources. Based on this study, we used a modified architecture of EfficientNet for semantic segmentation. The network architecture backbone is shown in tab:efficientNet, where each row describes a stage \(i\)  with \(\hat{L}_{i}\)  layers, with input resolution \(\langle \hat{H}_{i}, \hat{W}_{i}\rangle \)  and output channels \(\hat{C}_{i}\) . MBConv layers represent mobile inverted bottlenecks from the MobileNetV2 architecture [2]}, where squeeze-and-excitation optimization [3]} has also been added on top of it. To upsample the network output to its original input resolution a bilinear interpolation was used in the decoder architecture.
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